On the Classification of MR Images Using “ELM-SSA” Coated Hybrid Model

Ashwini Pradhan,Debahuti Mishra,Kaberi Das,Ganapati Panda,Mikhail Zymbler,Sachin Kumar

doi:10.3390/math9172095

Abstract

Computer-aided diagnosis permits biopsy specimen analysis by creating quantitative images of brain diseases which enable the pathologists to examine the data properly. It has been observed from other image classification algorithms that the Extreme Learning Machine (ELM) demonstrates superior performance in terms of computational efforts. In this study, to classify the brain Magnetic Resonance Images as either normal or diseased, a hybridized Salp Swarm Algorithm-based ELM (ELM-SSA) is proposed. The SSA is employed to optimize the parameters associated with ELM model, whereas the Discrete Wavelet Transformation and Principal Component Analysis have been used for the feature extraction and reduction, respectively. The performance of the proposed “ELM-SSA” is evaluated through simulation study and compared with the standard classifiers such as Back-Propagation Neural Network, Functional Link Artificial Neural Network, and Radial Basis Function Network. All experimental validations have been carried out using two different brain disease datasets: Alzheimer’s and Hemorrhage. The simulation results demonstrate that the “ELM-SSA” is potentially superior to other hybrid methods in terms of ROC, AUC, and accuracy. To achieve better performance, reduce randomness, and overfitting, each algorithm has been run multiple times and a k-fold stratified cross-validation strategy has been used.

Highlights

To facilitate doctors for diagnosis of brain disease, proper analysis and classification of various types of brain images are required
(vi) In general, the proposed Extreme Learning Machine (ELM)-Salp Swarm Algorithm (SSA) model outperforms other hybridized classification models such as FLANN-SSA with an improvement in accuracy of 5.31% and 1.02%
For extraction of features, (2) feature reduction employ using Principal Component Analysis (PCA), (3) overfitting control using stratified k-fold cross-validation, (4) development of ELM classifier, and (5) use of SSA to adjust the parameters of ELM

Summary

Introduction

To facilitate doctors for diagnosis of brain disease, proper analysis and classification of various types of brain images are required. The conventional images used for this purpose are Computed Tomography, Positron Emanation Tomography, Ultrasonography, X-radiation, and Magnetic Resonance Imaging (MRI). Out of these techniques, the MRI serves as a better source of information for brain study and it helps to recognize tissues with a higher spatial resolution. The MRI gives a better quality contrast for various cerebrum tissues and creates fewer antiquities [2,3,4] compared to other imaging processes. The CAD-based analysis from MR pictures has gained increasing importance among researchers [5]. For appropriate classification of MR pictures, the extracted features play an important role

Methods

Results

Conclusion